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Table Of Contents

Getting started with Julia Programming Language

By : Ivo Balbaert

4.1 (17)

Getting started with Julia Programming Language

4.1 (17)

By: Ivo Balbaert

Overview of this book

This book is for you if you are a data scientist or working on any technical or scientific computation projects. The book assumes you have a basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

The Rationale for Julia

The Rationale for Julia

The scope of Julia

Julia's place among the other programming languages

A comparison with other languages for the data scientist

Useful links

Summary

1. Installing the Julia Platform

1. Installing the Julia Platform

Installing Julia

Working with Julia's shell

Startup options and Julia scripts

Packages

Installing and working with Julia Studio

Installing and working with IJulia

Installing Sublime-IJulia

Installing Juno

Other editors and IDEs

How Julia works

Summary

2. Variables, Types, and Operations

2. Variables, Types, and Operations

Variables, naming conventions, and comments

Types

Integers

Floating point numbers

Elementary mathematical functions and operations

Rational and complex numbers

Characters

Strings

Regular expressions

Ranges and arrays

Dates and times

Scope and constants

Summary

3. Functions

3. Functions

Defining functions

Optional and keyword arguments

Anonymous functions

First-class functions and closures

Recursive functions

Map, filter, and list comprehensions

Generic functions and multiple dispatch

Summary

4. Control Flow

4. Control Flow

Conditional evaluation

Repeated evaluation

Exception handling

Scope revisited

Tasks

Summary

5. Collection Types

5. Collection Types

Matrices

Tuples

Dictionaries

Sets

Example project – word frequency

Summary

6. More on Types, Methods, and Modules

6. More on Types, Methods, and Modules

Type annotations and conversions

The type hierarchy – subtypes and supertypes

User-defined and composite types

Types and collections – inner constructors

Type unions

Parametric types and methods

Standard modules and paths

Summary

7. Metaprogramming in Julia

7. Metaprogramming in Julia

Expressions and symbols

Eval and interpolation

Defining macros

Built-in macros

Reflection capabilities

Summary

8. I/O, Networking, and Parallel Computing

8. I/O, Networking, and Parallel Computing

Basic input and output

Working with files

Using DataFrames

Working with TCP sockets and servers

Interacting with databases

Parallel operations and computing

Summary

9. Running External Programs

9. Running External Programs

Running shell commands

Calling C and FORTRAN

Calling Python

Performance tips

Summary

10. The Standard Library and Packages

10. The Standard Library and Packages

Digging deeper into the standard library

Julia's package manager

Publishing a package

Graphics in Julia

Using Gadfly on data

Summary

A. List of Macros and Packages

Macros

List of packages

Index

Index

Scope and constants

The region in the program where a variable is known is called the scope of that variable. Until now, we have only seen how to create top-level or global variables that are accessible from anywhere in the program. By contrast, variables defined in a local scope can only be used within that scope. A common example of a local scope is the code inside a function. Using global scope variables is not advisable for several reasons, notably the performance. If the value and type can change at any moment in the program, the compiler cannot optimize the code.

So, restricting the scope of a variable to local scope is better. This can be done by defining them within a function or a control construct, as we will see in the following chapters. This way, we can use the same variable name more than once without name conflicts.

Let's take a look at the following code fragment:

# code in chapter 2\scope.jl
x = 1.0 # x is Float64
x = 1 # now x is Int
# y::Float64 = 1.0 # LoadError: ...

CONTINUE READING

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Tech Concepts

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Programming languages

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